Okay, okay, good. So, hi everyone. My name is Renata, or Renata. I am a PhD student in theoretical neuroscience at Columbia University in the city of New York. And I'm here to tell you a little bit about what we did in the Datathon in team eight. This amazing people, Si Cheng Hao, Mona Patel, Jennifer Lee, Maya Sporn, Keegan Yap, and Jamie Sturgill. And, how do I move? Okay, so first of all, none of us have any conflicts of interest for this presentation. And our team was assigned to the COVID-19 track. And the initial question that we wanted to address was, do COVID-19 directed therapies have similar outcomes in patients of different ethnicities? And the reason behind this question is that it has been documented several disparities racial and ethnic disparities in several instances during the pandemic, including higher rates of hospitalization, admission to the ICU and mortality, and that have been documented in black and Latino patients. And so when we look at directed therapies for COVID-19, we have roughly pharmacological and non-pharmacological therapies. And normally pharmacological therapies like have a larger body of research on them. So we started looking more on the non-pharmacological therapies, and we found out that proning is largely under understood, under investigated because of the fact that proning is not an easily trackable variable because you don't have an order for proning. You don't have anything that ends up documented. So if you don't ask a professional specifically to record that information, you're not going to have this information. So it's not usual to have the information about proning on databases. And we had it on the virus registry. So we decided to focus on proning. And also during a datathon, you don't really have much time to do your research. You normally have one day and a half to decide what you're going to, the question that you want to address, decide how you want to address it, which data you're going to use, clean your data, select your cohort, prepare your data, analyze it, interpret it, and make a final presentation. So that's why we decided to focus on something specific so that we could do something new and contribute to the literature a little bit. So we know that proning works, but what we don't know is who's being proned and if proning works for everyone the same way. So our questions, just narrowing down to proning, we decided to focus on these two questions, which was, was there a difference in frequency of proning based on race and ethnicity? And question two, did this have an effect on the outcomes? Like different, did proning have a different effect, a different outcome in different, in patients of different ethnicities? So as I mentioned, we use the virus COVID-19 registry, which encompasses over 21,000 entries of ICU admissions over 21, 28 countries. And from these 21,000 entries, we selected patients that were over 18 and that were under invasive ventilation. We also decided to exclude pregnant subjects due to major differences in how COVID affects these group. And also we excluded patients involved in relevant eyes direct trials because we could not know who was under medication and who was a control. So we had no way to account for the effect of this potential medications. And also finally, we had to exclude a great number of subjects due to missingness of data of the variables that we were interested in, which were first day sofa, hospital discharge information, missing age and race. So looking at the demographics of our cohort, we had a large majority of white people. And also the vast, vast majority of our data, which ended up in 9,000 patients was from the Americas and specifically from the United States. So the demographics of global, uh, of the full cohort, including the whole world and of America are largely very similar, um, with the exception of a larger proportion of black patients in the American cohort and a smaller patient, uh, proportion of Asian patients. And off this final cohort, the global one, how many were prone? We found that we had, uh, around a third of, uh, our patients in this 9,000, uh, cohort were prone. And if we look at the demographics of the patients that were prone, it does look, uh, reasonably similar. But if we do a logistic regression, uh, adjusting for, uh, the severity, uh, which we use, uh, the first day of SOFA age and, um, sex, we saw that, uh, black patients were prone less than the rest. And interestingly, we also saw that females were prone less, uh, in the global context. And, uh, we also decided to look only in America and in America, we don't see this difference. Uh, black patients are not significantly prone less, they're actually prone a little bit more even though the effect here is not significant. Uh, so, uh, but females are still prone less. And this might be due to the fact that the probability of a patient being prone in centers outside of the United States is higher. Um, and also when we look at the probability of hospital mortality, we did not see, uh, especially, and when we look at overall prone, we did not see an effect of proning, uh, significant, uh, in decreasing mortality. This is the data from, from, I'm not saying that it's not, doesn't work, but this is what we saw in the data. And, um, also, uh, how can I go back? Okay, it's not that important. I was just gonna say that, uh, from that graph, uh, we can see that there were no differences also between, uh, patients of different ethnicities. Uh, and this is a very, uh, preliminary study that we did in, uh, one day and a half. Uh, and of course it's very limited also to the, to the selectiveness of the hospitals that could participate in a virus registry. And also, uh, a problem that we had was lack of detailed information from where the data came from. For example, we knew, uh, we only had the information of, uh, uh, continent level. So we only had Americas or Asia or, uh, we knew from, from the number of centers that the majority came from the United States, but would be very good to be able to narrow down from country and from hospital and maybe even from state. And I think the major problem also was data missingness because we, uh, uh, our initial cohort was, uh, much smaller than the number of entries in the dataset due to missingness of data. Uh, just going back to our main conclusions, we saw that there are differences in the incidence of proning based on race globally, but not in America. And we saw that in general, female patients were prone less in America and in the world and that race had no impact on mortality in patients who were prone in America and in the world as well. Uh, so as I said, this is a preliminary study, but it suggests, uh, how much, uh, prone is still largely under documented and requires more study and understanding. And we do plan to submit this as a manuscript and we're very glad to have met each other on the data thought it was an amazing experience. And thank you especially to all this amazing team, uh, to Ian and to everybody that made this data thon possible and made it possible for us to be telling you this here today. Thank you.

COVID-19 therapies

ethnic disparities

proning outcomes

mortality rates

data limitations